The technical and commercial success of cloud computing technology made it feasible to evolve the most demanding information and communication technology (ICT) infrastructures, such as communication networks, from specialized hardware and software to new software paradigms, referred to as ‘cloud-native’. Internet of Things (IoT) virtualization – IoT built on cloud-native principles – is to IoT platforms what Network Function Virtualization (NFV) is to communication networks.

We have covered the basic terms and definitions for data types and structure on my previous post, let’s dive into the creative and most time consuming side of data science — cleaning and feature engineering. What are some of the basic strategies that data scientists use to clean their data AND improve the amount of information they get from it?The type of cleaning and engineering strategies used usually depend on the business problem and type of target variable, since this will influence the algorithm and data preparation requirements.

With almost no industry untouched by blockchain-mania, what opportunities does the technology hold for the mobile industry? When we consider the application of blockchain to the telecoms industry, it is difficult to see large-scale disruption of existing operating models. This is because the industry has long-established supply chains and, in 3GPP, a well-functioning and transparent institution that fosters innovation and technology standardization. It is also an industry with a several decades of delivering cost reductions from scale economies. This limits the scope for blockchain to disrupt the established order

Whether it’s smart wearables, connected cars and machines, consumer electronics or smart city deployments, there is no denying the internet of things is growing at an increasingly rapid rate. However, yielding this opportunity in the correct way to create true value for enterprise and consumers while ensuring a safe and secure experience does not come without challenges. Addressing these is not something that device providers, service providers or application developers can ignore in their IoT strategies — but there is a solution.

If you are getting started in your data science journey and don’t come from a technical background, then you definitely understand the struggle of keeping up with the terminology of data pre-processing. This was obviously a concern, considering that Data Scientists spend 60% of the time cleaning and organizing data! This is the FIRST article, so we will only focus on key terms. Make sure to follow me, in order to read the next posts more focused on feature engineering, model selection, etc. Keep in mind that some of these terms differ depending on the language or platform you are using. But, I hope it gives you a nice overview.

How many people in the world do you think fit this bill? And what number of those people has the soft skills to be customer facing, client facing, and management facing, yet analytical, creative, and intelligent. We are asking the wrong things from Data Scientists and we are looking in the wrong places. There is no possible way that a Data Scientist will use all these tools at one company, and even less likely that someone knows all these languages. Data Science is more about the intelligent use of programming, rather than programming itself.

Artificial intelligence (AI) and machine learning have been around for years but more widely used in the business to consumer (B2C) space. f you’re in the business of lead generation in the B2B space, presenting offers that prospects are likely to act on is key. By learning user behavior and refining your digital experience, your marketing and sales approach is much more effective. In terms of B2B lead generation, this means using key metrics to identify your most valuable buyer personas.

In order to perform validation, you need data. More specifically, you need data with the information that you want to predict. We call this information the ground truth. Ground truth is usually provided by humans. In our processes, we believe that the ground truth is the actual value that we want to predict for our data. The adventure of validation begins once you have both your predictor and the data with ground truth. If the data with ground truth was not present to your development process, it is easy.

In this note, we’ll cover gradient descent algorithm and its variants: Batch Gradient Descent, Mini-batch Gradient Descent, and Stochastic Gradient Descent. Gradient Descent is the most common optimization algorithm in machine learning and deep learning. It is a first-order optimization algorithm. This means it only takes into account the first derivative when performing the updates on the parameters. Let’s first see how gradient descent and its associated steps work on logistic regression before going into the details of its variants.

There are many cases where projects that supposed to be very small, but development kept going for years; projects that designed as “fire and forget” ended up being very important for the organization. This is why even your shortest code should have good naming. If you never thought about better naming, hope after reading this you will try naming your entities better, and see how it improves the quality of your code. The habit of naming better might seem hard to build first. You may not want to spend your time on finding better names. However this is a habit that pays back. You should practice it even if the code you write is a prototype, or part of a tiny project.

There is lots of excitement about analytics and machine learning. The improvements in analytics, AI, and machine learning are amazing, but they provide you with numbers, not answers if they don’t solve business problems. It’s moving through its hype-cycle but still faces many challenges. Don’t get too excited about deploying an analytics solution. Make sure you know what business problems you want to solve. And make sure the solution helps you solve them. Real-Time analytics provides value at the point of activity within your current workflow or process.

Cognitive search offers the potential for dramatic improvements in the accuracy, relevance, and efficiency of insight discovery. Although some see cognitive search as simply traditional search enhanced by machine learning and artificial intelligence, there is actually a sophisticated combination of capabilities that make it distinct from and superior to traditional enterprise search. The cognitive search goes well beyond search engines to bring together myriad data sources, along with sophisticated tagging automation and personalization, vastly improving how an organization’s employees find, discover and access the information they need to do their jobs.

Artificial Intelligence (AI) is hardly in its infancy stage. It is not that accessible and thus the AI based solutions that we are using or are being deployed are far inferior to what we expect in the next two to three decades. Two AI bots could often fight each other if pitted that way, and this fight could last for centuries the way we look at AI right now. All this indicates that AI needs strong leadership; a leader who can set the direction, control and govern the very innovation.

The supply chain in the pharmaceutical industry is complex, with drugs changing ownership from manufacturers to distributors, repackagers, and wholesalers before reaching the customer. Consequences include the counterfeit drug problem and inefficient processes for conducting recalls and returns processing. These inefficiencies result in financial losses and loss of trust with consumers. The blockchain could be an opportunity platform to increase trust and transparency, with customers being able to track pharmaceutical products throughout the supply chain. Only trusted parties are granted access to write on the blockchain.

Most business problems can’t be turned into a game, however; you have more than two players and no clear rules. The outcomes of business decisions are rarely a clear win or loss, and there are far too many variables. So it’s a lot more difficult for businesses to implement AI than it seems. AI is advancing rapidly and will surely make it easier to clean up and integrate data. But business leaders will still need to understand what it really does and create a vision for its use. That is when they will see the big benefits.

Traditional IT infrastructure data flows would be transformed because data integrity could never be accidentally or maliciously altered while moving around in a multi-cloud environment, taking the concern of losing or compromising data completely off the table. If you could apply blockchain technology to the continual verifiability and integrity of your stored data before sending it from one site to another, the source site data could be checked against the target site data by sending the crypto-hash first. This would be a game-changer in accelerating the movement of data.

Software is everywhere in the modern business, and you need to develop, update and deploy it fast. A vital enabler of rapid software delivery is DevOps - the merging of application development and IT operations. It speeds up the creation and launch of new products, features and customer experiences, and ensures that they're as effective as possible. In the digital economy, DevOps underpins the flexibility you need to give customers what they want when they want it. The time for full DevOps is now.

Corporations still aren’t paying enough attention to cybersecurity issues; perhaps because there’s been a startling lack of real penalty for failing to protect information from hackers. There’s the lack of mandatory reporting and the limits of voluntary reporting. And the lack of real protection for the personal information we’ve entrusted to various companies. There’s a need to recognize that securing information is hard work on an ongoing basis. Senior executives and the board should be talking about the information security program at least annually. They need to overinvest for the next few years to recover from prior underinvestment

What are the characteristics of organizations that will be the ultimate winners in this Great AI War? What are the behaviors and actions that will distinguish those organizations that capitalize on this AI gold rush while others fumble the future? Leading AI organizations realize that data and analytics are unlike any traditional corporate assets. As the world prepares for the impending great AI war, now is not the time for organizations to be shy or to cling to old, outdated business models.

The insurance industry is rapidly evolving away from its traditional model of assessing future risks and pricing based on historical records and demographics. For decades, underwriters relied on this data to predict everything from an individual’s expected lifespan to the probability of a driver being involved in an accident. But, as it has in so many industries today, technology is disrupting this long-standing practice. In the case of insurance, the internet of things phenomenon has led this revolution.

The uptake of automation within the supply chain has, until recently, been slow. However, the development of new capabilities for automation technologies means that a growing number of companies globally are relying on RPA to streamline the flow of goods on their supply-side. But how is the leading technology trend poised to impact supply chain management? What are potential use cases as well as their logistical benefits? What can be expected of software robots in the future? Let’s look at the potential for automation within the supply chain.

Blockchain technology can solve development problems as it improves existing instruments and enables the development of new ones. Blockchain-based applications particularly address institutional weaknesses and financial inclusion because they restrict deception, corruption and uncertainties. In the future, the blockchain can also be a development vehicle empowering people directly and mitigating power asymmetries. The governments of underdeveloped countries should support the implementation of applications to benefit general development. They should, therefore, clarify legal frameworks and establish an encouraging business environment.

Data Quality Management is one of the key functions of the Data Governance to manage and improve the quality of data within the organization. Data quality remediation cannot be fully automated as there may be newer errors that need to be resolved through manual intervention. There are still a sizeable number of Data quality issues that can be automated in combination with a Machine learning capability.In this case, a cognitive Robotic process automation solution which combines machine learning capabilities and traditional RPA capabilities can be a potent solution for a faster remediation of data quality issues.

The artificial intelligence revolution is upon us. Automation, which once started as a desire to make mundane tasks easier, has advanced rapidly to create fundamental and beneficial changes to human life. Despite its widespread advantages, some have turned the discussion around AI into the negative. Doomsday scenarios in movies such as The Terminator have led to two main fears surrounding AI: its ability to be used for malicious purposes and the possibility that robots and computers could make significant changes to the world at humankind's expense.

Made in Boston @

The Harvard Innovation Lab

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